THE STATUTORY DERIVATIVE ACTION UNDER THE COMPANIES ACT OF 2008: THE ROLE OF GOOD FAITH
Bibliographic record
Abstract
The new statutory derivative action under the Companies Act 71 of 2008 is a paramount \nprotective measure or weapon for minority shareholders, which will be very useful in good \ncorporate governance and in policing boards of directors. The court is entrusted in terms of \ns 165 with a pivotal role as the gatekeeper, and has a crucial screening function in the \nexercise of its discretion to grant leave to a minority shareholder (or other applicant) to \ninstitute derivative litigation to seek redress for the company, when those in control of it \nimproperly fail or refuse to do so. The approach that the courts adopt to the application of \nthe three guiding criteria in s 165(5)(b) for the exercise of their discretion—particularly \nthe open-textured criterion of ‘good faith’— is a matter of supreme importance that will \nhave a major impact on the effectiveness (or lack thereof) of the new statutory derivative \naction. The focus of this article is this particularly elusive criterion of good faith, and its \nmany nuances, interpretations and applications in relevant foreign jurisdictions. A \nframework for good faith in South African law is proposed, and further fundamental facets \nof good faith are explored, with reference both to existing principles in our common law and \nvaluable lessons gleaned from other comparable jurisdictions such as Canada, Australia, \nNew Zealand and the United Kingdom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".